基于稀疏嵌入的多分类脑电信号分类方法研究
Research on multi-classification EEG signal classification method based on sparse embedding
郑旭 1王延平 1高诺1
作者信息
- 1. 山东建筑大学 信息与电气工程学院,济南 250101
- 折叠
摘要
为解决运动想象脑电(electroencephalogram,EEG)信号多分类传输速率慢、准确率低的问题,本研究利用"一对多"滤波组共空间模式(one vs rest filter bank common spatial pattern,OVR-FBCSP)和稀疏嵌入(sparse embeddings,SE)提出了一种基于SE的多分类EEG信号分类方法.为降低多类任务特征提取的复杂度,提高分类效率,本方法首先采用OVR-FBCSP进行EEG信号特征提取;然后对其相应的标签矩阵进行低维嵌入,构建稀疏嵌入模型,分别计算训练和测试数据的嵌入矩阵;最后在嵌入空间中对训练和测试数据执行k最近邻(k-nearest neighbor,kNN)分类.本研究在BCI Competition IV-2a公开数据集进行了实验测试,并与其他分类方法进行了对比.实验结果表明,本研究方法拥有较高的分类准确率和较短的分析时间.
Abstract
In order to solve the slow transmission rate and low classification accuracy,we proposed a multi-classification method for electroencephalogram(EEG)signals by using one vs rest filter bank common spatial pattern(OVR-FBCSP)and sparse embeddings(SE).For reducing the complexity of multi-task feature extraction and improving the classification efficiency,the OVR-FBCSP was used to extract EEG feature.Then,the corresponding label matrix was embedded in low dimension,the SE model was constructed,and the embedding matrix of the training and test data were calculated respectively.Finally,k-nearest neighbor(kNN)classification was performed for training and test data in the embedding space.The experiment was tested on the BCI Competition IV-2a open data set and compared with other classification methods.Experimental results show that the proposed method has higher classification accuracy and shorter analysis time.
关键词
运动想象/稀疏嵌入/一对多共空间模式/k最近邻Key words
Motor imagery/Sparse embedding/One vs rest common spatial pattern/K-nearest neighbor引用本文复制引用
基金项目
山东省自然科学基金资助项目(ZR2022MF309)
山东省科技型中小企业创新能力提升工程项目(2022TSGC2554)
出版年
2024
国家科技期刊平台
NSTL
万方数据